Depth-based 6-DoF Grasping: 6-DoF grasping plays a more critical role in the grasping community, allowing robots to plan higher dexterous grasps. Two ways exist in the depth processing: one represented by [1]} directly uses a depth image and leverages both 2D detection and accurate depth to estimate the rotated angles and gripper depth to recover 6-DoF poses. However, due to the top-down view and eye-on-hand requirement, some constraints arising from the pose representation persist. This makes it unsuitable in practice for some positions. Consistent with planar grasping, it is enough for the bin-picking task, but the performance is limited for more complex manipulations. The other way which has surged to solve the problem is based on point cloud processing represented by a large body of method [2]}, [3]}, [4]}, [5]}, [6]} using 3D backbones [7]}, [8]}, [9]}. They aim to achieve so-called “AnyGrasp" performance. The success rate of these approaches is high when the depth is reliable. Due to the strong dependency on the depth sensor, however, the performance drops severely for photometrically challenging objects.
